The neoplastic process that results in incurable cancer is a series of deviations from the normal homeostatic system. The goal of this project is to integrate traditionally biologic measures of these deviations with nanotechnology through computational approaches. The central hypothesis and working model on which this project is based is that time series data contains patterns that, when identified by computational means, can be used for early detection, prognosis, and/or evaluation of the response to anti-cancer therapy. Two types of cancer will be used to address this hypothesis: Chronic Lymphocytic Leukemia (CLL) and breast cancer. For each there is extensive basic and clinical research expertise at the UCSD Cancer Center, which is home for large longitudinal sample collections and clinical information databases. Two mouse models actively under study complement the human patient resources. The first aim of this project is to validate a role for longitudinal measures accomplished with nanodevices in the prediction, prognosis, and/or treatment of tumors in these mouse models. Transgenic mice predisposed to develop breast cancer or leukemia will be assessed for a broad spectrum of tumor and host response indicators during the time of tumor development. The data from the mouse studies will feed into the second aim, which is to create computational models to integrate biologic data with data from nano-sensors. The Multivariate State Estimation Technique (MSET) is an exquisitely precise mathematical system for real-time process monitoring and extraction of anomalous responses based on prior observations and will be the primary machine-learning algorithm employed. Finally, this project will leverage the information gained in the first two aims to validate ex vivo platforms for the monitoring of CLL and breast cancer, using our large clinical sample collections. This research will benefit public in two ways. Most directly, it will allow for better clinical evaluation of patients with breast cancer and CLL using new nanotechnologies. More generally, it will establish a new paradigm for detecting and monitoring cancer that allow for earlier, more focused treatments that are less debilitating and ultimately more effective than current anti-cancer therapies.